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• W4312852028 abstract "Uncovered muck truck detection aims to detect the muck truck and distinguish whether it is covered or not by dust-proof net to trace the source of pollution. Unlike traditional detection problem, recalling all uncovered trucks is more important than accurate locating for pollution traceability. When two objects are very close in an image, the occluded object may not be recalled because the non-maximum suppression (NMS) algorithm can remove the overlapped proposal. To address this issue, we propose a Location First NMS method to match the ground truth boxes and predicted boxes by position rather than class identifier (ID) in the training stage. Firstly, a box matching method is introduced to re-assign the predicted box ID using the closest ground truth one, which can avoid object missing when the IoU of two proposals is greater than the threshold. Secondly, we design a loss function to adapt the proposed algorithm. Thirdly, a uncovered muck truck detection system is designed using the method in a real scene. Experiment results show the effectiveness of the proposed method." @default.
• W4312852028 created "2023-01-05" @default.
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• W4312852028 creator A5079902952 @default.
• W4312852028 date "2023-06-01" @default.
• W4312852028 modified "2023-09-26" @default.
• W4312852028 title "Location First Non-Maximum Suppression for Uncovered Muck Truck Detection" @default.
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• W4312852028 doi "https://doi.org/10.1587/transfun.2022eap1100" @default.
• W4312852028 hasPublicationYear "2023" @default.
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